Perceptions, experiences, barriers, facilitators, learning outcomes, and modes of assessment of digital clinical placements for pre-registration physiotherapy students internationally: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: The shift to digital clinical placements for physiotherapy education due to COVID-19 prompts a need for evaluation of current evidence. Existing studies highlight benefits of digital technology in clinical placements, but lack of a systematic review focused on pre-registration physiotherapy students is a key gap. This systematic review will address this gap by synthesizing the evidence for digital clinical placements for pre-registration physiotherapy students internationally. METHODS AND ANALYSIS: This systematic review is designed using the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) statement and Cochrane Handbook - it is registered on PROSPERO (CRD42024571696). Search terms will be adapted to each database, including EMBASE, MEDLINE, PROSPERO, ERIC, and CINAHL. Key journals, forward citation tracking, references of included studies, and professional organization websites will also be searched. The search will include studies published since database inception to 31/05/24. There will be no limit to study design or language. Studies that report on perceptions, experiences, barriers, facilitators, learning outcomes, and modes of assessment of digital clinical placements for pre-registration physiotherapy students will be included. Meta-aggregation will be used to synthesize themes from findings which enables the generation of themes without the need to re-interpret data and the loss of study specific context. ETHICS AND DISSEMINATION: Ethics approval is not required. The results of this study will be written up for publication in relevant peer-reviewed scientific journals and contribute to a developing area of research. Results will also be presented at national or international conferences, events for the physiotherapy profession, or education events.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.095 | 0.083 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.014 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".